Say No to Freeloader: Protecting Intellectual Property of Your Deep Model

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Say No to Freeloader: Protecting Intellectual Property of Your Deep Model
Title:
Say No to Freeloader: Protecting Intellectual Property of Your Deep Model
Journal Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keywords:
Publication Date:
26 August 2024
Citation:
Wang, L., Wang, M., Fu, H., & Zhang, D. (2024). Say No to Freeloader: Protecting Intellectual Property of Your Deep Model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–14. https://doi.org/10.1109/tpami.2024.3450282
Abstract:
Model intellectual property (IP) protection has attracted growing attention as science and technology advancements stem from human intellectual labor and computational expenses. Ensuring IP safety for trainers and owners is of utmost importance, particularly in domains where ownership verification and applicability authorization are required. A notable approach to safeguarding model IP involves proactively preventing the use of well-trained models of authorized domains from unauthorized domains. In this paper, we introduce a novel Compact Un-transferable Pyramid Isolation Domain (CUPI-Domain) which serves as a barrier against illegal transfers from authorized to unauthorized domains. Drawing inspiration from human transitive inference and learning abilities, the CUPI-Domain is designed to obstruct cross-domain transfers by emphasizing the distinctive style features of the authorized domain. This emphasis leads to failure in recognizing irrelevant private style features on unauthorized domains. To this end, we propose novel CUPI-Domain generators, which select features from both authorized and CUPI-Domain as anchors. Then, we fuse the style features and semantic features of these anchors to generate labeled and style-rich CUPI-Domain. Additionally, we design external Domain-Information Memory Banks (DIMB) for storing and updating labeled pyramid features to obtain stable domain class features and domain class-wise style features. Based on the proposed whole method, the novel style and discriminative loss functions are designed to effectively enhance the distinction in style and discriminative features between authorized and unauthorized domains, respectively. Moreover, we provide two solutions for utilizing CUPI-Domain based on whether the unauthorized domain is known: target-specified CUPI-Domain and target-free CUPI-Domain. By conducting comprehensive experiments on various public datasets, we validate the effectiveness of our proposed CUPI-Domain approach with different backbone models. The results highlight that our method offers an efficient model intellectual property protection solution.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Central Research Fund
Grant Reference no. : NA

This research / project is supported by the A*STAR - Career Development Fund
Grant Reference no. : C222812010
Description:
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2160-9292
1939-3539
0162-8828
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